Triple
T13867969
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Ramanagara district |
E333376
|
entity |
| Predicate | hasTown |
P847
|
FINISHED |
| Object |
Harohalli
Harohalli is a town in the Indian state of Karnataka, known for its industrial area and proximity to Bengaluru.
|
E1068836
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Harohalli | Statement: [Ramanagara district, hasTown, Harohalli]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Harohalli Context triple: [Ramanagara district, hasTown, Harohalli]
-
A.
Devanahalli
Devanahalli is a town near Bengaluru in the Indian state of Karnataka, notable for its rapid development and proximity to Kempegowda International Airport.
-
B.
Nayandahalli
Nayandahalli is a locality in southwestern Bangalore known as a key junction and residential area along major transport routes.
-
C.
Lakkundi
Lakkundi is a historic village in Karnataka, India, renowned for its intricately carved medieval temples and stepwells built during the Western Chalukya period.
-
D.
Halasuru
Halasuru is a historic neighborhood in eastern Bengaluru, India, known for its temples, markets, and proximity to Ulsoor Lake.
-
E.
Gorikot
Gorikot is a village and local hub in Pakistan’s Astore Valley, serving as a gateway to nearby mountain regions and trekking routes.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Harohalli Triple: [Ramanagara district, hasTown, Harohalli]
Generated description
Harohalli is a town in the Indian state of Karnataka, known for its industrial area and proximity to Bengaluru.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Harohalli Target entity description: Harohalli is a town in the Indian state of Karnataka, known for its industrial area and proximity to Bengaluru.
-
A.
Devanahalli
Devanahalli is a town near Bengaluru in the Indian state of Karnataka, notable for its rapid development and proximity to Kempegowda International Airport.
-
B.
Nayandahalli
Nayandahalli is a locality in southwestern Bangalore known as a key junction and residential area along major transport routes.
-
C.
Lakkundi
Lakkundi is a historic village in Karnataka, India, renowned for its intricately carved medieval temples and stepwells built during the Western Chalukya period.
-
D.
Halasuru
Halasuru is a historic neighborhood in eastern Bengaluru, India, known for its temples, markets, and proximity to Ulsoor Lake.
-
E.
Gorikot
Gorikot is a village and local hub in Pakistan’s Astore Valley, serving as a gateway to nearby mountain regions and trekking routes.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69d81c5ced9c8190b0e9bcc6effe5959 |
completed | April 9, 2026, 9:38 p.m. |
| NER | Named-entity recognition | batch_69de05c530148190b11704300bbd5f9b |
completed | April 14, 2026, 9:15 a.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69f7c70e48788190997562e045f3b014 |
completed | May 3, 2026, 10:07 p.m. |
| NEDg | Description generation | batch_69f7c8d477f881908f8cfd2783e7f10f |
completed | May 3, 2026, 10:14 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69f7ca27ffd4819080bccd6bfd88ddb3 |
completed | May 3, 2026, 10:20 p.m. |
Created at: April 9, 2026, 10:14 p.m.